UniFORM: Towards Universal ImmunoFluorescence Normalization for Multiplex Tissue Imaging.

ABSTRACT: Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including Z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline that uses an automated rigid landmark functional data registration approach for normalizing both feature- and pixel-level MTI data. Designed specifically for the distributional characteristics of MTI datasets, UniFORM operates without prior distributional assumptions and performs robustly regardless of distribution modality, including

SUBMITTER: Wang K 

PROVIDER: S-EPMC11661071 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature